| 1 | Venskus, Julius; Jurkus, Robertas; Treigys, Povilas. Confidence and prediction intervals usage in maritime traffic awareness evaluation using LSTM deep neural networks // DAMSS: 14th conference on data analysis methods for software systems, Druskininkai, Lithuania, November 30 - December 2, 2023. Vilnius : Vilniaus universiteto leidykla, 2023. eISBN 9786090709856. p. 95-96. (Vilnius University Proceedings, eISSN 2669-0233 ; vol. 39). DOI: 10.15388/DAMSS.14.2023. |
| 2 | Jurkus, Robertas; Venskus, Julius; Treigys, Povilas. Categorical data encoding techniques for recursive multi-step prediction of vessel trajectory // ITISE 2023: 9th International conference on Time Series and Forecasting, 11th-14th July , 2023, Gran Canaria (Spain) : program and abstracts. 2023, abstract no. 7920, p. 78-79. Prieiga per internetą: <https://itise.ugr.es/ITISE2023_Program_Abstracts.pdf>. |
| 3 | Jurkus, Robertas; Venskus, Julius; Treigys, Povilas. Application of coordinate systems for vessel trajectory prediction improvement using a recurrent neural networks // Engineering applications of artificial intelligence. Oxford : Pergamon-Elsevier Science Ltd. ISSN 0952-1976. eISSN 1873-6769. 2023, vol. 123, part C, art. no. 106448, p. [1-10]. DOI: 10.1016/j.engappai.2023.106448. |
| 4 | Jurkus, Robertas; Venskus, Julius; Treigys, Povilas. Laivo tipų įtaka prognozuojant laivo judėjimo trajektoriją naudojant giliuosius rekurentinius neuroninius tinklus // Technologijų ir verslo aktualijos – 2022: studentų mokslinių darbų konferencijos pranešimų medžiaga, 2022 m. lapkričio 25 d., Panevėžys, Lietuva. Kaunas : Kauno technologijos universitetas. 2022, p. [1-9]. |
| 5 | Jurkus, Robertas; Treigys, Povilas; Venskus, Julius. Prediction of vessels trajectory using different coordinate systems // DAMSS: 12th conference on data analysis methods for software systems, Druskininkai, Lithuania, December 2–4, 2021. Vilnius : Vilnius University Press, 2021. ISBN 9786090706732. eISBN 9786090706749. p. 28. DOI: 10.15388/DAMSS.12.2021. |
| 6 | Jurkus, Robertas; Treigys, Povilas; Venskus, Julius. Investigation of recurrent neural network architectures for prediction of vessel trajectory // Information and software technologies : 27th international conference, ICIST 2021, Kaunas, Lithuania, October 14-16, 2021 : proceedings / A. Lopata, D. Gudonienė, R. Butkienė (eds.). Cham : Springer, 2021. ISBN 9783030883034. eISBN 9783030883041. p. 194-208. (Communications in Computer and Information Science book series (CCIS), ISSN 1865-0929, eISSN 1865-0937 ; vol. 1486). DOI: 10.1007/978-3-030-88304-1_16. |
| 7 | Markevičiūtė, Jurgita; Bernatavičienė, Jolita; Levulienė, Rūta; Medvedev, Viktor; Treigys, Povilas; Venskus, Julius. Attention-based and time series models for short-term forecasting of COVID-19 spread // CMC-Computers, materials & continua. Henderson, NV : TECH Science Press. ISSN 1546-2218. eISSN 1546-2226. 2022, vol. 70, no. 1, p. 695-714. DOI: 10.32604/cmc.2022.018735. |
| 8 | Venskus, Julius; Treigys, Povilas; Bernatavičienė, Jolita; Markevičiūtė, Jurgita. Detecting Maritime traffic anomalies with long-short term memory recurrent neural network // 11th international workshop on data analysis methods for software systems (DAMSS 2019), Druskininkai, Lithuania, November 28-30, 2019 / Lithuanian Computer Society, Vilnius University Institute of Data Science and Digital Technologies, Lithuanian Academy of Sciences. Vilnius : Vilnius University Press, 2019. ISBN 9786090703243. eISBN 9786090703250. p. 89. DOI: 10.15388/Proceedings.2019.8. |
| 9 | Venskus, Julius; Treigys, Povilas. Preparation of training data by filling in missing vessel type data using deep multi-stacked LSTM neural network for abnormal marine transport evaluation // ITISE 2019 : International Conference on Time Series and Forecasting : proceedings of abstracts. Granada, Spain, September, 25-27, 2019. Granada : Universidad de Granada, 2019. ISBN 9788417970796. p. 38. |
| 10 | Venskus, Julius; Treigys, Povilas. Meteorological data influence on missing Vessel type detection using deep Multi-Stacked LSTM neural network // Computer data analysis and modeling: stochastics and data science : proceedings of the XII international conference, Minsk, September 18-22, 2019. Minsk : Belarusian State University, 2019. ISBN 9789855668115. p. 307-310. Prieiga per internetą: <http://elib.bsu.by/handle/123456789/233315>. |
| 11 | Venskus, Julius; Treigys, Povilas; Bernatavičienė, Jolita; Tamulevičius, Gintautas; Medvedev, Viktor. Real-time maritime traffic anomaly detection based on sensors and history data embedding // Sensors. Basel : MDPI. ISSN 1424-8220. 2019, vol. 19, no. 17, art. no. 3782, p. 1-10. DOI: 10.3390/s19173782. |
| 12 | Venskus, Julius; Treigys, Povilas; Bernatavičienė, Jolita; Andziulis, Arūnas. Aspects of data collection for abnormal marine transport evaluation // DAMSS 2018 : 10th international workshop on "Data analysis methods for software systems", Druskininkai, Lithuania, November 29 - December 1, 2018 : [abstract book]. Vilnius : Vilniaus universitetas, 2018. ISBN 9786090700433. p. 88. Prieiga per internetą: <https://www.mii.lt/datamss/files/DAMSS_2018_1.pdf>. |
| 13 | Venskus, Julius; Treigys, Povilas; Bernatavičienė, Jolita; Medvedev, Viktor. Retraining strategies of modified SOM for abnormal marine traffic detection // 9th International workshop on Data Analysis Methods for Software Systems (DAMSS), Druskininkai, Lithuania, November 30 - December 2, 2017. Vilnius : VIlniaus universitetas, 2017. ISBN 9789986680642. p. 54. Prieiga per internetą: <https://www.mii.lt/datamss/files/liks_mii_drusk_2017.pdf>. |
| 14 | Venskus, Julius; Treigys, Povilas; Bernatavičienė, Jolita; Medvedev, Viktor; Voznak, Miroslav; Kurmis, Mindaugas; Bulbenkienė, Violeta. Integration of a self-organizing map and a virtual pheromone for real-time abnormal movement detection in marine traffic // Informatica. Vilnius : Vilniaus universiteto Matematikos ir informatikos institutas. ISSN 0868-4952. eISSN 1822-8844. 2017, vol. 28, no. 2, p. 359-374. DOI: 10.15388/Informatica.2017.133. |
| 15 | Venskus, Julius; Kurmis, Mindaugas; Treigys, Povilas. Modified SOM for abnormal marine traffic detection // Data analysis methods for software systems : 8th international workshop on data analysis methods for software systems : Druskininkai, December 1-3, 2016. Vilnius : Vilniaus universiteto leidykla, 2016. ISBN 9789986680611. p. 66-67. DOI: 10.15388/DAMSS.2016. |